'If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown,' says Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications (NCSA), and the principal investigator of the new study.

The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more.

'Deep learning approaches have just started to be applied for agricultural applications, and we foresee a huge potential of such technologies for future innovations in this area,' says Jian Peng, assistant professor in the Department of Computer Science at U of I, and a co-author and co-principal investigator of the new study.

Even though it was a relatively small area, analyzing 15 years of satellite data at a 30-meter resolution still required a supercomputer to process tens of terabytes of data.

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But the new technique can distinguish the two major crops with 95 percent accuracy by the end of July for each field – just two or three months after planting and well before harvest.

The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more.

“Deep learning approaches have just started to be applied for agricultural applications, and we foresee a huge potential of such technologies for future innovations in this area,” says Jian Peng, assistant professor in the Department of Computer Science at U of I, and a co-author and co-principal investigator of the new study.

Even though it was a relatively small area, analyzing 15 years of satellite data at a 30-meter resolution still required a supercomputer to process tens of terabytes of data.

“Technology wise, being able to handle such a huge amount of data and apply an advanced machine-learning algorithm was a big challenge before, but now we have supercomputers and the skills to handle the dataset.” The team is now working on expanding the study area to the entire Corn Belt, and investigating further applications of the data, including yield and other quality estimates.

Source: Lauren Quinn and Kaiyu Guan, University of Illinois “If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown,” says Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications (NCSA), and the principal investigator of the new study. The

researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more. For

Image credit: Cai et al., 2018A set of satellites known as Landsat have been continuously circling the Earth for [over] 40 years, collecting images using sensors that represent different parts of the electromagnetic spectrum.

The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more.

“Deep learning approaches have just started to be applied for agricultural applications, and we foresee a huge potential of such technologies for future innovations in this area,” says Jian Peng, assistant professor in the Department of Computer Science at U of I, and a co-author and co-principal investigator of the new study.

“Technology wise, being able to handle such a huge amount of data and apply an advanced machine-learning algorithm was a big challenge before, but now we have supercomputers and the skills to handle the dataset.” The team is now working on expanding the study area to the entire Corn Belt, and investigating further applications of the data, including yield and other quality estimates.

'If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown,' says Kaiyu Guan, Blue Waters professor at the National Center for Supercomputing Applications (NCSA), assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, and the principal investigator of the new study.

More timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity market future projections, and more.

Guan says most previous attempts to differentiate corn and soybean from these images were based on the visible and near-infrared part of the spectrum signal analysis, but his team decided to include something different.

These powerful computing resources at NCSA allowed Guan and his team to determine how the deep learning approach can be used for crop-type classification, and how early into the growing season this method can be applied for optimal accuracy of crop-type classification.

To validate the technique, the team used short-wave infrared (SWIR) data and other spectral data from three Landsat satellites over a 15-year period, and consistently picked up this leaf water status signal.

'Deep learning approaches have just started to be applied for agricultural applications, and we foresee a huge potential of such technologies for future innovations in this area,' says Jian Peng, a NCSA Faculty Fellow and assistant professor in the Department of Computer Science at University of Illinois, and a co-author and co-principal investigator of the new study.

Even though it was a relatively small area, analyzing 15 years of satellite data at a 30-meter resolution still required a supercomputer to process tens of terabytes of data.

Recent advances that were not possible without these resources include computationally designing the first set of antibody prototypes to detect the Ebola virus, simulating the HIV capsid, visualizing the formation of the first galaxies and exploding stars, and understanding how the layout of a city can impact supercell thunderstorms.

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